Biomedical　Question　Answering　aims　to　extract　an　answer　to　the　given　question　from　a　biomedical　context.　Due　to　the　strong　professionalism　of　specific　domain,　it＇s　more　difficult　to　build　large-scale　datasets　for　specific　domain　question　answering.　Existing　methods　are　limited　by　the　lack　of　training　data,　and　the　performance　is　not　as　good　as　in　open-domain　settings,　especially　degrading　when　facing　to　the　adversarial　sample.　We　try　to　resolve　the　above　issues.　First,　effective　data　augmentation　strategies　are　adopted　to　improve　the　model　training,　including　slide　window,　summarization　and　round-trip　translation.　Second,　we　propose　a　model　weighting　strategy　for　the　final　answer　prediction　in　biomedical　domain,　which　combines　the　advantage　of　two　models,　open-domain　model　QANet　and　BioBERT　pre-trained　in　biomedical　domain　data.　Finally,　we　give　adversarial　training　to　reinforce　the　robustness　of　the　model.　The　public　biomedical　dataset　collected　from　PubMed　provided　by　BioASQ　challenge　is　used　to　evaluate　our　approach.　The　results　show　that　the　model　performance　has　been　improved　significantly　compared　to　the　single　model　and　other　models　participated　in　BioASQ　challenge.　It　can　learn　richer　semantic　expression　from　data　augmentation　and　adversarial　samples,　which　is　beneficial　to　solve　more　complex　question　answering　problems　in　biomedical　domain.